Linear Regression vs. Graph Neural Network
August 4, 2025
\[ \text{tmpf}_{t+1}=\text{features}_t+\text{features}_{t-1}+...+\text{features}_{t-27} \]
Where:
\[ \text{features}_t=\text{\{tmpf, }\text{relh, }\text{sknt, }\text{drct}_{sin}\text{, drct}_{cos}\text{\}} \]
tmpf: Temperature
relh: Relative Humidity
sknt: Wind Speed
\(\text{drct}_{sin}\text{, drct}_{cos}\text{: }\)Wind Direction encoded as sine and cosine components
| Feature | Description |
|---|---|
| station | Station identifier code (3-4 characters) |
| valid | Timestamp of the observation |
| lon | Longitude |
| lat | Latitude |
| elevation | Elevation in feet |
| tmpf | Air temperature (F) |
| relh | Relative humidity (%) |
| drct | Wind direction (degrees) |
| sknt | Wind speed (knots) |
| p01i | Precipitations (inches) over the previous hour |
| vsby | visibility (miles) |
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Below is an example of the data requiring both spatial and temporal imputation: Below is the same data post imputation:
Below shows the inter-node correlation: Below shows the intra-node correlation: